Data-driven strategies using customer intent trends | Part 1

At Figaro Digital Marketing Summit our very own Head of Marketing, Sophie Moule, demonstrated how vital customer data is in informing not just our digital, but our entire business strategy.

In the first part of this article, we’ll revisit the search trends insights explored in Sophies talk across the ‘Beauty sector’, and uncover how to use customer behaviour data proactively to spot patterns and forecast strategy.

In the second part, we’ll show how beneficial it is to explore market share of voice data to give context to search trends, and create agile (Plan, Influence, Peak, Repeat) strategies to involve the whole business in future-planning.

Think of customer needs first, and tech later

As a constantly evolving landscape, digital can be increasingly challenging to strategise for.

Fashion UK | Leaderboard

Customer behaviour data:

In the next four steps, we’ll show you how to put customer behaviour and search trend data at the heart of your digital strategy, to fuel success throughout your business.

Discovering customer trends

Use search trend data – Google trends is a good starting point.

Amalgamate key searches into categories and plot them onto a search volume chart over time.

This methodology immediately reveals seasonal insights – especially in retail – but this can be applied to most sectors.

Discovering customer trends in the ‘Beauty’ sector

In this chart:

Beauty sector searches in 2017.

We can already spot some areas of interest in the ‘Beauty’ market.

‘Hair care’ is the top performing category for 10/12ths of the year.

‘Nails’ peak in summer and christmas during the presumed party seasons.

‘Fragrance’ searches peak drastically at the end of the year, in the lead up to Christmas.

2.Commercialising customer trends

It’s important not to look at search volume alone. We can be more intelligent with additional data points.

We apply OVS (which is our own proprietary formula), to overlay a monetary value to search trends.

This enables businesses to look beyond just eyeballs on content, and get a truer idea of a search’s intrinsic value and propensity to convert.

Essentially, it takes the remaining adwords factors, such as CPC and competition, into consideration alongside search volume.

Applying this formula to every search term across the landscape (in this instance – ‘Beauty’), we can spot which searches and categories are more commercially valuable than others.

Most companies want to focus their efforts on the areas that will deliver the highest ROI.

Commercialising customer trends in the ‘Beauty’ sector

In this chart:

We can see how trends throughout the year change when value is overlaid.

‘Electrical’ rises to the top.

‘Hair care’, on the other hand, drops in value to the 4th category.

‘Makeup’ remains one of the most important areas.

Analysing customer trends

While it’s great to see a year of behaviour data, and while you can still gain intelligent insight at this level, to achieve organisational buy-in we’ll need to highlight patterns as well as trends.

We can do this by looking back at multiple years to see if we can spot patterns, similarities and shifts in the market.

Analysing customer trends in the ‘Beauty’ sector

In this chart:

We can see four years of search trends across all categories, based on value

‘Body care’ gradually increases YoY

We can also begin to spot patterns…

‘Fragrance’ snake-heads at the end of every year – not just 2017 – and growth occurs as early as April.

‘Makeup’ growth is significant, and has tripled from 2014 – 2017.

We can also draw conclusions on the overall market, as growth occurs across every category.

We shouldn’t just think of this as being a pool of arbitrary searches, or techy search data.

As we can see, ‘Makeup’ grew dramatically between 2015 and 2016. With our overlay of value, we know buying intent existed and we know this wasn’t a mistake.

In fact, this was a whole new market of buyers shopping for ‘Makeup products’. If we match this with what we already know about the market, we can make intelligent assumptions, i.e. that growth coincided with the rise of the ‘Blogger’ or ‘Vlogger’ and online endorsements.

And once we’ve spotted growth, we can dive-deeper into sub-categories to see growth across niche areas. For example, in ‘Makeup’ we discovered most growth was driven by ‘Face makeup’ specifically.

This is real business intelligence which spells the difference between an organisation being agile and successful, or simply being another player struggling to carve a place for itself in a competitive sector.

Predicting customer trends

Finally, when we have a few years of data, we can make intelligent predictions on best and worst case scenarios for each trend line in our market (i.e. by plussing or minussing 20%).

What’s more, we can start thinking about who in our business can use this opportunity data for their own future-planning.